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Great package and enjoy how well/fast the RcppML package performs!
I was just wondering if addition of a consensus map, similar to the ones implemented by brunet https://doi.org/10.1073/pnas.0308531101, would be useful here!
Regards,
ttj131
The text was updated successfully, but these errors were encountered:
It could be helpful, simply as a sanity check for whether your model is behaving properly. What I mean, is that factors should not be similar to one another -- the whole objective of NMF is to separate distinct signals, particularly if you apply some small amount of sparsifying regularization (such as L2).
If you want to find similar factors across equal rank models, use bipartite matching (Hungarian algorithm). RcppML::bipartiteMatch.
If you want to find similar factors across different models, that is where consensus maps would be most helpful.
Hi!
Great package and enjoy how well/fast the RcppML package performs!
I was just wondering if addition of a consensus map, similar to the ones implemented by brunet https://doi.org/10.1073/pnas.0308531101, would be useful here!
Regards,
ttj131
The text was updated successfully, but these errors were encountered: